
import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_    :			Any    =	logging.get_logger(__name__)







def 							A__       (    lowerCamelCase    )	->						Any:
				print("""Loading config file..."""    )

				def flatten_yaml_as_dict(lowerCamelCase  ,     lowerCamelCase=""  ,     lowerCamelCase="."    ):
								UpperCamelCase_:				int						=    []
								for k, v in d.items():
												UpperCamelCase_:				int						=    parent_key + sep + k if parent_key else k
												if isinstance(lowerCamelCase  ,     collections.abc.MutableMapping    ):
																items.extend(flatten_yaml_as_dict(lowerCamelCase  ,     lowerCamelCase  ,     sep=lowerCamelCase    ).items()    )
												else:
																items.append((new_key, v)    )
								return dict(lowerCamelCase    )

				UpperCamelCase_:				Dict						=    argparse.Namespace()
				with open(lowerCamelCase  ,     """r"""    ) as yaml_file:
								try:
												UpperCamelCase_:				Dict						=    yaml.load(lowerCamelCase  ,     Loader=yaml.FullLoader    )

												UpperCamelCase_:				str						=    flatten_yaml_as_dict(lowerCamelCase    )
												for k, v in flat_cfg.items():
																setattr(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
								except yaml.YAMLError as exc:
												logger.error("""Error while loading config file: {}. Error message: {}""".format(lowerCamelCase  ,     str(lowerCamelCase    )    )    )
				return config







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				UpperCamelCase_:				int						=    MobileViTVaConfig()

				UpperCamelCase_:				List[Any]						=    False

				# dataset
				if task_name.startswith("""imagenet1k_"""    ):
								UpperCamelCase_:				Optional[int]						=    10_00
								if int(task_name.strip().split("""_"""    )[-1]    ) == 3_84:
												UpperCamelCase_:				Tuple						=    3_84
								else:
												UpperCamelCase_:				str						=    2_56
								UpperCamelCase_:				List[Any]						=    """imagenet-1k-id2label.json"""
				elif task_name.startswith("""imagenet21k_to_1k_"""    ):
								UpperCamelCase_:				Optional[Any]						=    2_10_00
								if int(task_name.strip().split("""_"""    )[-1]    ) == 3_84:
												UpperCamelCase_:				int						=    3_84
								else:
												UpperCamelCase_:				Optional[Any]						=    2_56
								UpperCamelCase_:				List[str]						=    """imagenet-22k-id2label.json"""
				elif task_name.startswith("""ade20k_"""    ):
								UpperCamelCase_:				List[Any]						=    1_51
								UpperCamelCase_:				Union[str, Any]						=    5_12
								UpperCamelCase_:				List[str]						=    """ade20k-id2label.json"""
								UpperCamelCase_:				str						=    True
				elif task_name.startswith("""voc_"""    ):
								UpperCamelCase_:				Optional[Any]						=    21
								UpperCamelCase_:				List[Any]						=    5_12
								UpperCamelCase_:				Dict						=    """pascal-voc-id2label.json"""
								UpperCamelCase_:				str						=    True

				# orig_config
				UpperCamelCase_:				List[str]						=    load_orig_config_file(lowerCamelCase    )
				assert getattr(lowerCamelCase  ,     """model.classification.name"""  ,     -1    ) == "mobilevit_v2", "Invalid model"
				UpperCamelCase_:				Optional[Any]						=    getattr(lowerCamelCase  ,     """model.classification.mitv2.width_multiplier"""  ,     1.0    )
				assert (
				    getattr(lowerCamelCase  ,     """model.classification.mitv2.attn_norm_layer"""  ,     -1    ) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				UpperCamelCase_:				Union[str, Any]						=    getattr(lowerCamelCase  ,     """model.classification.activation.name"""  ,     """swish"""    )
				# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

				if is_segmentation_model:
								UpperCamelCase_:				int						=    getattr(lowerCamelCase  ,     """model.segmentation.output_stride"""  ,     16    )
								if "_deeplabv3" in task_name:
												UpperCamelCase_:				Any						=    getattr(lowerCamelCase  ,     """model.segmentation.deeplabv3.aspp_rates"""  ,     [12, 24, 36]    )
												UpperCamelCase_:				Optional[Any]						=    getattr(lowerCamelCase  ,     """model.segmentation.deeplabv3.aspp_out_channels"""  ,     5_12    )
												UpperCamelCase_:				Any						=    getattr(lowerCamelCase  ,     """model.segmentation.deeplabv3.aspp_dropout"""  ,     0.1    )

    # id2label
				UpperCamelCase_:				Union[str, Any]						=    """huggingface/label-files"""
				UpperCamelCase_:				Tuple						=    json.load(open(hf_hub_download(lowerCamelCase  ,     lowerCamelCase  ,     repo_type="""dataset"""    )  ,     """r"""    )    )
				UpperCamelCase_:				List[Any]						=    {int(lowerCamelCase    ): v for k, v in idalabel.items()}
				UpperCamelCase_:				Optional[Any]						=    idalabel
				UpperCamelCase_:				List[str]						=    {v: k for k, v in idalabel.items()}

				return config







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				UpperCamelCase_:				Any						=    dct.pop(lowerCamelCase    )
				UpperCamelCase_:				Any						=    val







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=False    )	->						Dict:
				if base_model:
								UpperCamelCase_:				str						=    """"""
				else:
								UpperCamelCase_:				Optional[int]						=    """mobilevitv2."""

				UpperCamelCase_:				Tuple						=    []
				for k in state_dict.keys():
								if k[:8] == "encoder.":
												UpperCamelCase_:				int						=    k[8:]
								else:
												UpperCamelCase_:				int						=    k

								if ".block." in k:
												UpperCamelCase_:				Optional[int]						=    k_new.replace(""".block."""  ,     """."""    )
								if ".conv." in k:
												UpperCamelCase_:				Tuple						=    k_new.replace(""".conv."""  ,     """.convolution."""    )
								if ".norm." in k:
												UpperCamelCase_:				Optional[int]						=    k_new.replace(""".norm."""  ,     """.normalization."""    )

								if "conv_1." in k:
												UpperCamelCase_:				List[Any]						=    k_new.replace("""conv_1."""  ,     F'''{model_prefix}conv_stem.'''    )
								for i in [1, 2]:
												if F'''layer_{i}.''' in k:
																UpperCamelCase_:				List[str]						=    k_new.replace(F'''layer_{i}.'''  ,     F'''{model_prefix}encoder.layer.{i-1}.layer.'''    )
								if ".exp_1x1." in k:
												UpperCamelCase_:				Dict						=    k_new.replace(""".exp_1x1."""  ,     """.expand_1x1."""    )
								if ".red_1x1." in k:
												UpperCamelCase_:				int						=    k_new.replace(""".red_1x1."""  ,     """.reduce_1x1."""    )

								for i in [3, 4, 5]:
												if F'''layer_{i}.0.''' in k:
																UpperCamelCase_:				Union[str, Any]						=    k_new.replace(F'''layer_{i}.0.'''  ,     F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''    )
												if F'''layer_{i}.1.local_rep.0.''' in k:
																UpperCamelCase_:				str						=    k_new.replace(F'''layer_{i}.1.local_rep.0.'''  ,     F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''    )
												if F'''layer_{i}.1.local_rep.1.''' in k:
																UpperCamelCase_:				str						=    k_new.replace(F'''layer_{i}.1.local_rep.1.'''  ,     F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''    )

								for i in [3, 4, 5]:
												if i == 3:
																UpperCamelCase_:				Optional[int]						=    [0, 1]
												elif i == 4:
																UpperCamelCase_:				Optional[int]						=    [0, 1, 2, 3]
												elif i == 5:
																UpperCamelCase_:				Any						=    [0, 1, 2]

												for j in j_in:
																if F'''layer_{i}.1.global_rep.{j}.''' in k:
																				UpperCamelCase_:				List[Any]						=    k_new.replace(
																				    F'''layer_{i}.1.global_rep.{j}.'''  ,     F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''    )
												if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
																UpperCamelCase_:				List[Any]						=    k_new.replace(
																    F'''layer_{i}.1.global_rep.{j+1}.'''  ,     F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''    )

												if F'''layer_{i}.1.conv_proj.''' in k:
																UpperCamelCase_:				Tuple						=    k_new.replace(F'''layer_{i}.1.conv_proj.'''  ,     F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''    )

								if "pre_norm_attn.0." in k:
												UpperCamelCase_:				Optional[Any]						=    k_new.replace("""pre_norm_attn.0."""  ,     """layernorm_before."""    )
								if "pre_norm_attn.1." in k:
												UpperCamelCase_:				List[str]						=    k_new.replace("""pre_norm_attn.1."""  ,     """attention."""    )
								if "pre_norm_ffn.0." in k:
												UpperCamelCase_:				Optional[Any]						=    k_new.replace("""pre_norm_ffn.0."""  ,     """layernorm_after."""    )
								if "pre_norm_ffn.1." in k:
												UpperCamelCase_:				List[Any]						=    k_new.replace("""pre_norm_ffn.1."""  ,     """ffn.conv1."""    )
								if "pre_norm_ffn.3." in k:
												UpperCamelCase_:				Tuple						=    k_new.replace("""pre_norm_ffn.3."""  ,     """ffn.conv2."""    )

								if "classifier.1." in k:
												UpperCamelCase_:				List[Any]						=    k_new.replace("""classifier.1."""  ,     """classifier."""    )

								if "seg_head." in k:
												UpperCamelCase_:				Any						=    k_new.replace("""seg_head."""  ,     """segmentation_head."""    )
								if ".aspp_layer." in k:
												UpperCamelCase_:				str						=    k_new.replace(""".aspp_layer."""  ,     """."""    )
								if ".aspp_pool." in k:
												UpperCamelCase_:				List[Any]						=    k_new.replace(""".aspp_pool."""  ,     """."""    )

								rename_keys.append((k, k_new)    )
				return rename_keys







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				Tuple						=    []
				for k in state_dict.keys():
								if k.startswith("""seg_head.aux_head."""    ):
												keys_to_ignore.append(lowerCamelCase    )
				for k in keys_to_ignore:
								state_dict.pop(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    )	->						List[Any]:
				UpperCamelCase_:				List[Any]						=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
				# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
				UpperCamelCase_:				List[Any]						=    Image.open(requests.get(lowerCamelCase  ,     stream=lowerCamelCase    ).raw    )
				return im







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Tuple:
				UpperCamelCase_:				Tuple						=    get_mobilevitva_config(lowerCamelCase  ,     lowerCamelCase    )

				# load original state_dict
				UpperCamelCase_:				Optional[int]						=    torch.load(lowerCamelCase  ,     map_location="""cpu"""    )

				# load huggingface model
				if task_name.startswith("""ade20k_"""    ) or task_name.startswith("""voc_"""    ):
								UpperCamelCase_:				Tuple						=    MobileViTVaForSemanticSegmentation(lowerCamelCase    ).eval()
								UpperCamelCase_:				int						=    False
				else:
								UpperCamelCase_:				Tuple						=    MobileViTVaForImageClassification(lowerCamelCase    ).eval()
								UpperCamelCase_:				Optional[Any]						=    False

				# remove and rename some keys of load the original model
				UpperCamelCase_:				List[Any]						=    checkpoint
				remove_unused_keys(lowerCamelCase    )
				UpperCamelCase_:				Tuple						=    create_rename_keys(lowerCamelCase  ,     base_model=lowerCamelCase    )
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# load modified state_dict
				model.load_state_dict(lowerCamelCase    )

				# Check outputs on an image, prepared by MobileViTImageProcessor
				UpperCamelCase_:				Optional[Any]						=    MobileViTImageProcessor(crop_size=config.image_size  ,     size=config.image_size + 32    )
				UpperCamelCase_:				int						=    image_processor(images=prepare_img()  ,     return_tensors="""pt"""    )
				UpperCamelCase_:				Dict						=    model(**lowerCamelCase    )

				# verify classification model
				if task_name.startswith("""imagenet"""    ):
								UpperCamelCase_:				Union[str, Any]						=    outputs.logits
								UpperCamelCase_:				Optional[int]						=    logits.argmax(-1    ).item()
								print("""Predicted class:"""  ,     model.config.idalabel[predicted_class_idx]    )
								if task_name.startswith("""imagenet1k_256"""    ) and config.width_multiplier == 1.0:
												# expected_logits for base variant
												UpperCamelCase_:				Optional[Any]						=    torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]    )
												assert torch.allclose(logits[0, :3]  ,     lowerCamelCase  ,     atol=1E-4    )

				Path(lowerCamelCase    ).mkdir(exist_ok=lowerCamelCase    )
				print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''    )
				model.save_pretrained(lowerCamelCase    )
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
				image_processor.save_pretrained(lowerCamelCase    )


if __name__ == "__main__":
							lowerCamelCase_    :			List[str]    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--task""",
							    default="""imagenet1k_256""",
							    type=str,
							    help=(
							        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
							        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
							    ),
							    choices=[
							        """imagenet1k_256""",
							        """imagenet1k_384""",
							        """imagenet21k_to_1k_256""",
							        """imagenet21k_to_1k_384""",
							        """ade20k_deeplabv3""",
							        """voc_deeplabv3""",
							    ],
							)

							parser.add_argument(
							    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
							)
							parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
							)

							lowerCamelCase_    :			Optional[Any]    =	parser.parse_args()
							convert_mobilevitva_checkpoint(
							    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
							)








import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






